Site Characteristics
| LENO |
18.1 |
1386 |
American Sweetgum, American Hornbeam, Possumhaw |
44.7 +- 13.8 |
1.0 +- 0.4 |
| DELA |
17.6 |
1372 |
Water oak, Red maple, Sugarberry |
37.0 +- 6.3 |
0.88 +- 0.2 |
| ORNL |
14.4 |
1340 |
Red maple, Sour gum, Chestnut oak |
22.0 +- 12.7 |
0.13 +- 0.05 |
| SERC |
13.6 |
1075 |
Tulip poplar, American Beech, American Sweetgum |
17.4 +- 6.2 |
0.27 +- 0.1 |
| HARV |
7.4 |
1199 |
Eastern Hemlock, Northern Red Oak, American
Beech |
5.5 +- 1.9 |
0.38 +- 0.1 |
| BART |
6.2 |
1325 |
American Beech, Eastern Hemlock, Red maple |
3.6 +- 0.7 |
0.76 +- 0.5 |
| TREE |
4.8 |
797 |
Sugar maple, Red maple, Gray alder |
5.4 +- 1.1 |
0.34 +- 0.09 |
|
|
proportion MAOM C
|
[MAOM C]
|
proportion MAOM N
|
[MAOM N]
|
|
Predictors
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
df
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
df
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
std. p
|
df
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
df
|
|
Intercept
|
0.80
|
0.04
|
0.00
|
0.15
|
<0.001
|
41.00
|
30.15
|
7.53
|
-0.00
|
0.24
|
<0.001
|
41.00
|
0.88
|
0.04
|
0.02
|
0.11
|
<0.001
|
0.847
|
40.00
|
1.18
|
0.34
|
-0.00
|
0.18
|
0.001
|
41.00
|
|
Mycorrhizal dominance
|
-0.10
|
0.03
|
-0.42
|
0.13
|
0.002
|
41.00
|
-1.96
|
3.05
|
-0.07
|
0.10
|
0.524
|
41.00
|
-0.19
|
0.07
|
-0.23
|
0.12
|
0.010
|
0.061
|
40.00
|
-0.14
|
0.19
|
-0.08
|
0.11
|
0.453
|
41.00
|
|
CDI
|
0.15
|
1.23
|
0.02
|
0.18
|
0.905
|
41.00
|
-583.93
|
212.12
|
-0.69
|
0.25
|
0.009
|
41.00
|
0.72
|
1.16
|
0.50
|
0.13
|
0.536
|
0.001
|
40.00
|
-15.46
|
9.69
|
-0.32
|
0.20
|
0.118
|
41.00
|
|
FeOx
|
0.01
|
0.00
|
0.50
|
0.16
|
0.003
|
41.00
|
1.36
|
0.33
|
0.62
|
0.15
|
<0.001
|
41.00
|
0.00
|
0.00
|
0.09
|
0.14
|
0.517
|
0.517
|
40.00
|
0.11
|
0.02
|
0.88
|
0.15
|
<0.001
|
41.00
|
Mycorrhizal dominance * CDI
|
|
|
|
|
|
|
|
|
|
|
|
|
4.33
|
1.99
|
0.24
|
0.11
|
0.036
|
0.036
|
40.00
|
|
|
|
|
|
|
|
Random Effects
|
|
σ2
|
0.00
|
36.91
|
0.00
|
0.14
|
|
τ00
|
0.00 Site
|
27.96 Site
|
0.00 Site
|
0.04 Site
|
|
ICC
|
0.09
|
0.43
|
|
0.24
|
|
N
|
7 Site
|
7 Site
|
7 Site
|
7 Site
|
|
Observations
|
47
|
47
|
47
|
47
|
|
Marginal R2 / Conditional R2
|
0.330 / 0.391
|
0.359 / 0.635
|
0.429 / NA
|
0.474 / 0.602
|
|
|
proportion oPOM C
|
[oPOM C]
|
proportion oPOM N
|
[oPOM N]
|
|
Predictors
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
df
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
df
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
std. p
|
df
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
df
|
|
Intercept
|
0.06
|
0.03
|
0.00
|
0.18
|
0.039
|
41.00
|
3.55
|
1.33
|
0.00
|
0.19
|
0.011
|
41.00
|
0.03
|
0.03
|
-0.03
|
0.13
|
0.227
|
0.847
|
40.00
|
0.13
|
0.04
|
0.00
|
0.20
|
0.005
|
41.00
|
|
Mycorrhizal dominance
|
0.05
|
0.02
|
0.33
|
0.15
|
0.027
|
41.00
|
1.25
|
0.80
|
0.20
|
0.13
|
0.127
|
41.00
|
0.11
|
0.04
|
0.18
|
0.14
|
0.016
|
0.191
|
40.00
|
0.02
|
0.03
|
0.09
|
0.13
|
0.494
|
41.00
|
|
CDI
|
0.48
|
0.84
|
0.12
|
0.20
|
0.574
|
41.00
|
-79.58
|
37.57
|
-0.44
|
0.21
|
0.040
|
41.00
|
0.10
|
0.69
|
-0.40
|
0.16
|
0.881
|
0.014
|
40.00
|
-2.62
|
1.20
|
-0.48
|
0.22
|
0.035
|
41.00
|
|
FeOx
|
-0.00
|
0.00
|
-0.17
|
0.18
|
0.347
|
41.00
|
0.12
|
0.08
|
0.25
|
0.17
|
0.148
|
41.00
|
0.00
|
0.00
|
0.09
|
0.16
|
0.590
|
0.590
|
40.00
|
0.00
|
0.00
|
0.30
|
0.18
|
0.099
|
41.00
|
Mycorrhizal dominance * CDI
|
|
|
|
|
|
|
|
|
|
|
|
|
-2.56
|
1.18
|
-0.28
|
0.13
|
0.036
|
0.036
|
40.00
|
|
|
|
|
|
|
|
Random Effects
|
|
σ2
|
0.00
|
2.63
|
0.00
|
0.00
|
|
τ00
|
0.00 Site
|
0.56 Site
|
0.00 Site
|
0.00 Site
|
|
ICC
|
0.10
|
0.18
|
|
0.19
|
|
N
|
7 Site
|
7 Site
|
7 Site
|
7 Site
|
|
Observations
|
47
|
47
|
47
|
47
|
|
Marginal R2 / Conditional R2
|
0.101 / 0.190
|
0.215 / 0.353
|
0.246 / NA
|
0.189 / 0.346
|
|
|
proportion.C_FLF
|
mg.C.per.g.soil_FLF
|
proportion.N_FLF
|
mg.N.per.g.soil_FLF
|
|
Predictors
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
df
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
df
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
df
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
df
|
|
(Intercept)
|
0.137
|
0.029
|
-0.001
|
0.132
|
<0.001
|
41.000
|
4.720
|
1.502
|
-0.009
|
0.216
|
0.003
|
41.000
|
0.119
|
0.021
|
0.000
|
0.122
|
<0.001
|
41.000
|
0.174
|
0.051
|
-0.009
|
0.211
|
0.001
|
41.000
|
|
E
|
0.051
|
0.023
|
0.280
|
0.127
|
0.033
|
41.000
|
1.011
|
0.745
|
0.162
|
0.119
|
0.182
|
41.000
|
0.027
|
0.018
|
0.198
|
0.128
|
0.129
|
41.000
|
0.019
|
0.027
|
0.091
|
0.124
|
0.471
|
41.000
|
|
CDI
|
-0.715
|
0.809
|
-0.136
|
0.154
|
0.382
|
41.000
|
-87.344
|
42.376
|
-0.480
|
0.233
|
0.046
|
41.000
|
-1.610
|
0.577
|
-0.400
|
0.143
|
0.008
|
41.000
|
-3.068
|
1.429
|
-0.491
|
0.229
|
0.038
|
41.000
|
|
FeOx
|
-0.007
|
0.002
|
-0.498
|
0.150
|
0.002
|
41.000
|
-0.021
|
0.078
|
-0.044
|
0.164
|
0.791
|
41.000
|
-0.003
|
0.002
|
-0.241
|
0.145
|
0.105
|
41.000
|
0.000
|
0.003
|
0.008
|
0.170
|
0.965
|
41.000
|
|
Random Effects
|
|
σ2
|
0.002
|
2.223
|
0.001
|
0.003
|
|
τ00
|
0.000 Site
|
0.951 Site
|
0.000 Site
|
0.001 Site
|
|
ICC
|
0.031
|
0.300
|
|
0.261
|
|
N
|
7 Site
|
7 Site
|
7 Site
|
7 Site
|
|
Observations
|
47
|
47
|
47
|
47
|
|
Marginal R2 / Conditional R2
|
0.346 / 0.366
|
0.265 / 0.485
|
0.333 / NA
|
0.233 / 0.433
|
#what % of total C and N was MAOM C in each site?
pct.MAOM.C=final_data %>%
group_by(Site) %>%
mutate(mean.C.pro.maom=mean(proportion.C_HF),
se.pro.maom=sd(proportion.C_HF)/sqrt(46)) %>%
dplyr::select(plotID,mean.C.pro.maom, se.pro.maom)
## Adding missing grouping variables: `Site`
pct.MAOM.N=final_data %>%
group_by(Site) %>%
mutate(mean.N.pro.maom=mean(proportion.N_HF),
se.pro.maom.N=sd(proportion.N_HF)/sqrt(46)) %>%
dplyr::select(plotID,mean.N.pro.maom, se.pro.maom.N)
## Adding missing grouping variables: `Site`
# what % of total C and N was MAOM at the lowest %ECM and at the highest %ECM in each site?
end_members_maom_prop=final_data %>%
select(Site,Plot.x,E, proportion.C_HF, proportion.N_HF) %>%
arrange(Site,E) %>%
group_by(Site) %>%
mutate(myc_rank=order(E, decreasing=T)) %>%
mutate(max_A=max(myc_rank)) %>%
filter(myc_rank==max_A) %>%
ungroup() %>%
mutate(mean_prop_C=mean(proportion.C_HF),
mean_prop_N=mean(proportion.N_HF))
#At max ECM dominance, avg. proportion C in MAOM was 0.75831, N was 0.85565
#At max AM dominance, avg proportion C in MAOM was 0.83787, N was 0.89959
#from AM to ECM, MAOM C prop dropped 7.956%, N dropped 4.39%

concC.fe.hf=ggplot(final_data, aes(x=FeOx, y=mg.C.per.g.soil_HF, color=Site))+
geom_smooth(method="lm",size=2, se=F)+
geom_point()+
scale_colour_viridis_d(option="C")+
labs(x=" FeOx (mg/g soil)", y="[MAOM C] (mg/g soil)", colour="Site")+
theme_cowplot()+
theme(legend.title=element_text(hjust=0.5, size=16), legend.text=element_text(size=15), axis.text=element_text(size=15), axis.title=element_text(size=19))
propC.fe.hf=ggplot(final_data, aes(x=FeOx, y=proportion.C_HF, color=Site))+
geom_smooth(method="lm",size=2, se=F)+
geom_point()+
scale_colour_viridis_d(option="C")+
labs(x=" FeOx (mg/g soil)", y=expression("proportion C"[MAOM]), colour="Site")+
theme_cowplot()+
theme(legend.title=element_text(hjust=0.5, size=16), legend.text=element_text(size=15), axis.text=element_text(size=15), axis.title=element_text(size=19))
concN.fe.hf=ggplot(final_data, aes(x=FeOx, y=mg.N.per.g.soil_HF, color=Site))+
geom_smooth(method="lm",size=2, se=F)+
geom_point()+
scale_colour_viridis_d(option="C")+
labs(x=" FeOx (mg/g soil)", y="[MAOM N] (mg/g soil)", colour="Site")+
theme_cowplot()+
theme(legend.title=element_text(hjust=0.5, size=16), legend.text=element_text(size=15), axis.text=element_text(size=15), axis.title=element_text(size=19))
ggarrange(concC.fe.hf,concN.fe.hf, nrow=1, ncol=2, common.legend=T, legend="right")
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'

ggsave("hf.conc.CN.fe.pdf",path="figures/", width= 20, height= 10, units="cm")
#what are the slopes of these lines?
fe_conchf_slopes_C=coef(lmList(mg.C.per.g.soil_HF~FeOx|Site , data = final_data))[2]
fe.mod1.slopes= as.data.frame(forests)%>%
arrange(forests) %>%
cbind(fe_conchf_slopes_C$FeOx) %>%
summarize(mean_slope=mean(fe_conchf_slopes_C$FeOx))
fe_conchf_slopes_N=coef(lmList(mg.N.per.g.soil_HF~FeOx|Site , data = final_data))[2]
fe.mod2.slopes= as.data.frame(forests)%>%
arrange(forests) %>%
cbind(fe_conchf_slopes_N$FeOx) %>%
summarize(mean_slope=mean(fe_conchf_slopes_N$FeOx))
## 1 2
## -26.40569 -26.45323
|
|
d13C
|
d14C
|
|
Predictors
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
|
(Intercept)
|
-26.25
|
0.24
|
-0.00
|
0.14
|
<0.001
|
38.71
|
51.93
|
-0.01
|
0.40
|
0.461
|
|
FeOx
|
0.07
|
0.02
|
0.64
|
0.20
|
0.002
|
1.89
|
1.39
|
0.19
|
0.14
|
0.185
|
|
CDI
|
-17.29
|
7.45
|
-0.45
|
0.19
|
0.026
|
-1579.98
|
1489.10
|
-0.44
|
0.42
|
0.296
|
|
E
|
-0.03
|
0.21
|
-0.02
|
0.15
|
0.904
|
-12.25
|
13.00
|
-0.10
|
0.10
|
0.353
|
|
Random Effects
|
|
σ2
|
0.16
|
564.42
|
|
τ00
|
0.00 Site
|
1567.09 Site
|
|
ICC
|
|
0.74
|
|
N
|
6 Site
|
6 Site
|
|
Observations
|
40
|
40
|
|
Marginal R2 / Conditional R2
|
0.230 / NA
|
0.089 / 0.759
|
|
|
d14C
|
d14C
|
d13C
|
d13C
|
|
Predictors
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
|
(Intercept)
|
-12.86
|
14.42
|
0.00
|
0.16
|
0.378
|
-56.20
|
74.97
|
0.00
|
0.16
|
0.458
|
-26.42
|
0.16
|
-0.00
|
0.16
|
<0.001
|
-27.08
|
0.80
|
-0.00
|
0.16
|
<0.001
|
|
mg C per g soil HF
|
0.21
|
0.74
|
0.05
|
0.16
|
0.775
|
|
|
|
|
|
-0.00
|
0.01
|
-0.01
|
0.16
|
0.973
|
|
|
|
|
|
|
proportion C HF
|
|
|
|
|
|
58.01
|
92.10
|
0.10
|
0.16
|
0.533
|
|
|
|
|
|
0.81
|
0.99
|
0.13
|
0.16
|
0.419
|
|
Observations
|
40
|
40
|
40
|
40
|
|
R2 / R2 adjusted
|
0.002 / -0.024
|
0.010 / -0.016
|
0.000 / -0.026
|
0.017 / -0.009
|
|
|
d14C
|
d14C
|
d14C
|
d14C
|
d14C
|
d14C
|
|
Predictors
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
|
(Intercept)
|
-96.48
|
51.54
|
-0.00
|
0.42
|
0.135
|
-27.34
|
22.13
|
0.00
|
0.43
|
0.284
|
43.34
|
26.87
|
-0.00
|
0.33
|
0.182
|
48.99
|
65.67
|
-0.00
|
0.46
|
0.510
|
-118.57
|
64.15
|
0.00
|
0.35
|
0.138
|
-7.89
|
32.31
|
-0.00
|
0.43
|
0.823
|
|
FeOx
|
4.69
|
5.25
|
0.41
|
0.45
|
0.422
|
0.52
|
2.99
|
0.13
|
0.74
|
0.870
|
5.40
|
6.35
|
0.43
|
0.50
|
0.443
|
-10.42
|
11.74
|
-0.48
|
0.54
|
0.440
|
6.62
|
6.00
|
0.66
|
0.59
|
0.332
|
7.68
|
9.80
|
0.44
|
0.56
|
0.490
|
|
E
|
-22.78
|
49.88
|
-0.21
|
0.45
|
0.672
|
17.43
|
45.33
|
0.28
|
0.74
|
0.720
|
-17.01
|
26.68
|
-0.32
|
0.50
|
0.558
|
-26.97
|
46.72
|
-0.31
|
0.54
|
0.604
|
115.10
|
68.60
|
1.00
|
0.59
|
0.169
|
-36.78
|
29.43
|
-0.70
|
0.56
|
0.300
|
|
Observations
|
7
|
7
|
7
|
6
|
7
|
6
|
|
R2 / R2 adjusted
|
0.187 / -0.219
|
0.156 / -0.267
|
0.480 / 0.219
|
0.226 / -0.290
|
0.420 / 0.130
|
0.344 / -0.093
|
|
|
d13C
|
d13C
|
d13C
|
d13C
|
d13C
|
d13C
|
|
Predictors
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
|
(Intercept)
|
-26.31
|
0.40
|
-0.00
|
0.44
|
<0.001
|
-27.23
|
0.46
|
0.00
|
0.32
|
<0.001
|
-27.17
|
0.46
|
-0.00
|
0.16
|
<0.001
|
-26.87
|
0.67
|
0.00
|
0.51
|
<0.001
|
-26.48
|
0.50
|
0.00
|
0.29
|
<0.001
|
-27.25
|
0.50
|
-0.00
|
0.31
|
<0.001
|
|
FeOx
|
0.00
|
0.04
|
0.01
|
0.48
|
0.981
|
0.05
|
0.06
|
0.42
|
0.56
|
0.489
|
0.30
|
0.11
|
0.67
|
0.25
|
0.053
|
0.05
|
0.12
|
0.24
|
0.59
|
0.713
|
-0.03
|
0.05
|
-0.30
|
0.49
|
0.571
|
0.28
|
0.15
|
0.73
|
0.40
|
0.167
|
|
E
|
-0.23
|
0.38
|
-0.29
|
0.48
|
0.579
|
0.57
|
0.94
|
0.34
|
0.56
|
0.577
|
-0.63
|
0.46
|
-0.34
|
0.25
|
0.239
|
-0.01
|
0.48
|
-0.02
|
0.59
|
0.978
|
0.57
|
0.53
|
0.53
|
0.49
|
0.341
|
0.15
|
0.46
|
0.14
|
0.40
|
0.759
|
|
Observations
|
7
|
7
|
7
|
6
|
7
|
6
|
|
R2 / R2 adjusted
|
0.084 / -0.374
|
0.521 / 0.281
|
0.877 / 0.815
|
0.061 / -0.565
|
0.611 / 0.416
|
0.664 / 0.440
|


ecm_order=received_samples_plot %>%
select(plotID, order) %>%
distinct()
## Adding missing grouping variables: `Site`
pctAngio=ggplot(plotPctAngio %>%
rename("Angiosperm"="fractionAngiosperm") %>%
mutate(Gymnosperm=1-Angiosperm,
plotID=as.character(plotID))%>%
pivot_longer(c(Angiosperm, Gymnosperm), names_to="gymAng", values_to="dominance") %>%
left_join(ecm_order) , aes(x=order, y=dominance, fill=gymAng))+
geom_bar(position="stack", stat="identity", colour="black")+
scale_fill_manual( values=c("gray90", "gray20"))+
xlab("Study Plot")+
ylab("Proportion of Basal Area")+
facet_wrap(~Site, scales="free_x")+
theme_cowplot()+
theme(axis.text.x = element_blank(), axis.ticks.x = element_blank(),strip.background = element_rect( fill="white"),legend.title=element_blank())
## Joining, by = "plotID"
ggsave("pctAngioGymno.jpg", path="figures/", width= 20, height= 16, units="cm")
BAbyMYC=ggplot(final_data %>%
left_join(ecm_order), aes(x=order, y=plotBA))+
geom_bar( stat="identity", colour="black")+
xlab("Study plots in order of increasing ECM dominance")+
ylab("Basal Area")+
facet_wrap(~Site, scales="free_x")+
theme_cowplot()+
theme(axis.text.x = element_blank(), axis.ticks.x = element_blank(),strip.background = element_rect( fill="white"),legend.title=element_blank())
## Joining, by = c("plotID", "Site")
ggsave("BAmyc.jpg", path="figures/", width= 20, height= 16, units="cm")
ggplot(final_data, aes(x=fractionAngiosperm, y=proportion.C_HF, colour=Site))+
geom_smooth(method="lm",size=2, se=F)+
geom_point(size=3, alpha=0.5)+
labs(x=" Angiosperm dominance ", y="MAOM C (proportion)", colour="Site")+
theme_cowplot()+
theme(legend.title=element_text(hjust=0.5, size=16), legend.text=element_text(size=15), axis.text=element_text(size=15), axis.title=element_text(size=19))
## `geom_smooth()` using formula 'y ~ x'

ggsave("maom.ang.prop.jpg", path="figures/", width= 19, height= 16, units="cm")
## `geom_smooth()` using formula 'y ~ x'
ggplot(final_data, aes(x=fractionAngiosperm, y=mg.C.per.g.soil_HF, colour=Site))+
geom_smooth(method="lm",size=2, se=F)+
geom_point(size=3, alpha=0.5)+
labs(x=" Angiosperm dominance ", y="[MAOM C] (mg/g soil)", colour="Site")+
theme_cowplot()+
theme(legend.title=element_text(hjust=0.5, size=16), legend.text=element_text(size=15), axis.text=element_text(size=15), axis.title=element_text(size=19))
## `geom_smooth()` using formula 'y ~ x'

ggsave("maom.ang.conc.jpg", path="figures/", width= 19, height= 16, units="cm")
## `geom_smooth()` using formula 'y ~ x'
|
|
mg.C.per.g.soil_HF
|
mg.N.per.g.soil_HF
|
|
Predictors
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
|
(Intercept)
|
15.364
|
3.487
|
0.017
|
0.285
|
<0.001
|
1.245
|
0.185
|
-0.002
|
0.204
|
<0.001
|
|
plotBA
|
0.539
|
1.329
|
0.055
|
0.135
|
0.687
|
-0.042
|
0.087
|
-0.074
|
0.154
|
0.632
|
|
Random Effects
|
|
σ2
|
51.014
|
0.251
|
|
τ00
|
40.908 Site
|
0.044 Site
|
|
ICC
|
0.445
|
0.150
|
|
N
|
7 Site
|
7 Site
|
|
Observations
|
47
|
47
|
|
Marginal R2 / Conditional R2
|
0.003 / 0.447
|
0.005 / 0.155
|
#Relationships between site-level climate decomposition index (CDI) and a) soil oxalate-extractable iron content, and b) total tree basal area in our study plots.
basal_area_cdi=ggplot(final_data, aes(x=CDI, y=plotBA))+
geom_point()+
stat_summary(fun= mean, fun.min=mean, fun.max=mean, geom="crossbar", width=0.0009, position="dodge")+
stat_summary(fun.data = mean_se, geom = "errorbar", width=0.0005, position = position_dodge(width = 0.0009))+
theme_cowplot()+
xlab("Climate Decomposition Index")+
ylab("Total Basal Area"
)
feox_cdi=ggplot(final_data, aes(x=CDI, y=FeOx))+
geom_point()+
stat_summary(fun= mean, fun.min=mean, fun.max=mean, geom="crossbar", width=0.0009, position="dodge")+
stat_summary(fun.data = mean_se, geom = "errorbar", width=0.0005, position = position_dodge(width = 0.0009))+
geom_smooth(method="lm")+
theme_cowplot()+
xlab("Climate Decomposition Index")+
ylab("Soil Oxalate-Extractable Iron"
)
ggarrange(basal_area_cdi, feox_cdi, ncol=2, nrow=1, labels= c("a","b"), legend=F)
## `geom_smooth()` using formula 'y ~ x'

ggsave("FigS2.pdf", path="figures/", width= 24, height= 12, units="cm")
summary(lm(plotBA~CDI,data=final_data )) #
##
## Call:
## lm(formula = plotBA ~ CDI, data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.7455 -0.7491 -0.1379 0.6662 2.1157
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.7240 0.4667 3.694 0.000596 ***
## CDI 0.5506 12.8459 0.043 0.966001
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9489 on 45 degrees of freedom
## Multiple R-squared: 4.082e-05, Adjusted R-squared: -0.02218
## F-statistic: 0.001837 on 1 and 45 DF, p-value: 0.966
summary(lm(FeOx~CDI,data=final_data )) #yes, more FeOx in warmer sites
##
## Call:
## lm(formula = FeOx ~ CDI, data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.5624 -2.8296 0.2597 1.6610 11.9572
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.093 1.821 -0.600 0.551306
## CDI 184.539 50.119 3.682 0.000617 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.702 on 45 degrees of freedom
## Multiple R-squared: 0.2315, Adjusted R-squared: 0.2144
## F-statistic: 13.56 on 1 and 45 DF, p-value: 0.0006175
summary(lm(mg.C.per.g.soil_HF~FeOx,data=final_data %>% filter(Site=="LENO") )) #yes within this site Fe Ox is a big driver of concentrations
##
## Call:
## lm(formula = mg.C.per.g.soil_HF ~ FeOx, data = final_data %>%
## filter(Site == "LENO"))
##
## Residuals:
## 1 2 3 4 5 6 7
## -4.0951 4.7057 -1.6980 2.2892 -1.6658 0.1938 0.2702
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.5803 2.5703 1.393 0.22239
## FeOx 0.9075 0.2157 4.207 0.00843 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.16 on 5 degrees of freedom
## Multiple R-squared: 0.7798, Adjusted R-squared: 0.7357
## F-statistic: 17.7 on 1 and 5 DF, p-value: 0.008429
summary(lm(mg.C.per.g.soil_HF~FeOx,data=final_data %>% filter(Site=="DELA") )) #yes within this site Fe Ox is a big driver of concentrations
##
## Call:
## lm(formula = mg.C.per.g.soil_HF ~ FeOx, data = final_data %>%
## filter(Site == "DELA"))
##
## Residuals:
## 1 2 3 4 5 6 7
## -1.8847 -0.2968 3.2730 3.2409 -4.2195 1.1282 -1.2411
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.6878 4.0998 0.656 0.5410
## FeOx 1.1080 0.4278 2.590 0.0488 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.016 on 5 degrees of freedom
## Multiple R-squared: 0.5729, Adjusted R-squared: 0.4875
## F-statistic: 6.707 on 1 and 5 DF, p-value: 0.04884
summary(lm(FeOx~E,data=final_data )) #no overall trend with FeOx and ECM dominance across sites
##
## Call:
## lm(formula = FeOx ~ E, data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.211 -2.730 -0.970 2.103 13.966
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.182 1.125 3.716 0.000557 ***
## E 2.301 1.933 1.190 0.240214
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.158 on 45 degrees of freedom
## Multiple R-squared: 0.03052, Adjusted R-squared: 0.008974
## F-statistic: 1.417 on 1 and 45 DF, p-value: 0.2402
summary(lm(FeOx~E,data=final_data %>% filter(Site=="LENO") )) # yes within LENO positive relationship between soil FeOx and ECM dominance
##
## Call:
## lm(formula = FeOx ~ E, data = final_data %>% filter(Site == "LENO"))
##
## Residuals:
## 1 2 3 4 5 6 7
## 6.4818 -0.3810 -0.9449 -2.4064 -1.4924 3.2959 -4.5530
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.001 2.782 1.438 0.2099
## E 11.881 4.206 2.825 0.0369 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.066 on 5 degrees of freedom
## Multiple R-squared: 0.6148, Adjusted R-squared: 0.5378
## F-statistic: 7.981 on 1 and 5 DF, p-value: 0.03689
summary(lm(FeOx~E,data=final_data %>% filter(Site=="DELA") )) # not at all within DELA
##
## Call:
## lm(formula = FeOx ~ E, data = final_data %>% filter(Site == "DELA"))
##
## Residuals:
## 1 2 3 4 5 6 7
## -5.2936 -0.5310 -0.5159 -0.7887 1.2474 2.2708 3.6111
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.768 1.978 4.433 0.00681 **
## E 1.162 4.219 0.275 0.79401
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.129 on 5 degrees of freedom
## Multiple R-squared: 0.01495, Adjusted R-squared: -0.1821
## F-statistic: 0.07586 on 1 and 5 DF, p-value: 0.794
#Site means of MAOM C and N concentrations and proportions, ordered by climate decomposition index
MAOM_C_conc_cdi=ggplot(final_data, aes(x=CDI, y=mg.C.per.g.soil_HF))+
geom_point()+
stat_summary(fun= mean, fun.min=mean, fun.max=mean, geom="crossbar", width=0.0009, position="dodge")+
stat_summary(fun.data = mean_se, geom = "errorbar", width=0.0005, position = position_dodge(width = 0.0009))+
geom_smooth(method="lm")+
theme_cowplot()+
xlab("Climate Decomposition Index")+
ylab("[MAOM C] (mg/g soil)"
)
MAOM_C_prop_cdi=ggplot(final_data, aes(x=CDI, y=proportion.C_HF))+
geom_point()+
stat_summary(fun= mean, fun.min=mean, fun.max=mean, geom="crossbar", width=0.0009, position="dodge")+
stat_summary(fun.data = mean_se, geom = "errorbar", width=0.0005, position = position_dodge(width = 0.0009))+
theme_cowplot()+
xlab("Climate Decomposition Index")+
ylab(expression("proportion C"[MAOM])
)
MAOM_N_conc_cdi=ggplot(final_data, aes(x=CDI, y=mg.N.per.g.soil_HF))+
geom_point()+
stat_summary(fun= mean, fun.min=mean, fun.max=mean, geom="crossbar", width=0.0009, position="dodge")+
stat_summary(fun.data = mean_se, geom = "errorbar", width=0.0005, position = position_dodge(width = 0.0009))+
theme_cowplot()+
xlab("Climate Decomposition Index")+
ylab("[MAOM N] (mg/g soil)"
)
MAOM_N_prop_cdi=ggplot(final_data, aes(x=CDI, y=proportion.N_HF))+
geom_point()+
stat_summary(fun= mean, fun.min=mean, fun.max=mean, geom="crossbar", width=0.0009, position="dodge")+
stat_summary(fun.data = mean_se, geom = "errorbar", width=0.0005, position = position_dodge(width = 0.0009))+
geom_smooth(method="lm")+
theme_cowplot()+
xlab("Climate Decomposition Index")+
ylab(expression("proportion N"[MAOM])
)
ggarrange(MAOM_C_conc_cdi, MAOM_C_prop_cdi, MAOM_N_conc_cdi, MAOM_N_prop_cdi, ncol=2, nrow=2, labels= c("a","b", "c", "d"), legend=F)
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'

ggsave("FigXXX.jpg", path="figures/", width= 24, height= 22, units="cm")
#Is the amount of oxalate-extractable iron in soil related to mycorrhizal type?
ggplot(final_data, aes(x=E, y=FeOx))+
geom_point(color="blue")+
geom_smooth(method="lm", color="blue")+
facet_wrap(~Site)+
theme_cowplot()
## `geom_smooth()` using formula 'y ~ x'

ggsave("ECM_vs_FeOx.jpg", path="figures/", width= 30, height= 24, units="cm")
## `geom_smooth()` using formula 'y ~ x'
ggplot(final_data, aes(x=CDI, y=FeOx))+
geom_point(color="blue")+
geom_smooth(method="lm", color="blue")+
theme_cowplot()
## `geom_smooth()` using formula 'y ~ x'

ggsave("CDI_vs_FeOx.pdf", path="figures/", width= 30, height= 24, units="cm")
## `geom_smooth()` using formula 'y ~ x'
|
|
C.N_FLF
|
C.N_OLF
|
C.N_HF
|
|
Predictors
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
df
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
df
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
df
|
|
(Intercept)
|
23.724
|
3.861
|
0.000
|
0.174
|
<0.001
|
41.000
|
22.582
|
5.534
|
-0.001
|
0.199
|
<0.001
|
41.000
|
23.195
|
2.405
|
-0.001
|
0.160
|
<0.001
|
41.000
|
|
E
|
7.898
|
2.508
|
0.413
|
0.131
|
0.003
|
41.000
|
12.146
|
3.133
|
0.494
|
0.127
|
<0.001
|
41.000
|
2.322
|
1.110
|
0.171
|
0.082
|
0.043
|
41.000
|
|
CDI
|
66.397
|
108.571
|
0.119
|
0.195
|
0.544
|
41.000
|
141.022
|
156.075
|
0.197
|
0.218
|
0.372
|
41.000
|
-299.874
|
67.826
|
-0.757
|
0.171
|
<0.001
|
41.000
|
|
FeOx
|
-0.641
|
0.245
|
-0.441
|
0.168
|
0.012
|
41.000
|
-0.428
|
0.319
|
-0.229
|
0.171
|
0.187
|
41.000
|
-0.013
|
0.118
|
-0.013
|
0.114
|
0.912
|
41.000
|
|
Random Effects
|
|
σ2
|
25.801
|
39.696
|
4.909
|
|
τ00
|
3.948 Site
|
10.891 Site
|
2.607 Site
|
|
ICC
|
0.133
|
0.215
|
0.347
|
|
N
|
7 Site
|
7 Site
|
7 Site
|
|
Observations
|
47
|
47
|
47
|
|
Marginal R2 / Conditional R2
|
0.241 / 0.341
|
0.220 / 0.388
|
0.611 / 0.746
|
|
|
C.N_HF
|
C.N_HF
|
C.N_HF
|
C.N_HF
|
C.N_HF
|
C.N_HF
|
C.N_HF
|
|
Predictors
|
Estimates
|
p
|
Estimates
|
p
|
Estimates
|
p
|
Estimates
|
p
|
Estimates
|
p
|
Estimates
|
p
|
Estimates
|
p
|
|
(Intercept)
|
20.25
|
0.001
|
16.55
|
0.002
|
13.96
|
0.003
|
10.98
|
<0.001
|
10.58
|
0.006
|
10.00
|
<0.001
|
9.77
|
<0.001
|
|
E
|
-2.03
|
0.701
|
3.25
|
0.347
|
4.18
|
0.299
|
3.25
|
0.221
|
4.71
|
0.331
|
-0.46
|
0.540
|
-0.76
|
0.183
|
|
Observations
|
7
|
6
|
6
|
7
|
7
|
7
|
7
|
|
R2 / R2 adjusted
|
0.032 / -0.162
|
0.221 / 0.026
|
0.262 / 0.078
|
0.281 / 0.138
|
0.188 / 0.025
|
0.080 / -0.104
|
0.323 / 0.188
|
`